An explanatory variable is an independent variable. Normally, these variables are any factors that can influence the outcome generated by the response variable. On the other hand, a response variable is a dependent factor that evaluates the results of a particular study. It is the specific factor and quantity through which some questions are generated.
It is important to distinguish between the dependent and the independent variables in the linear regression, as linear regression helps to predict the value of the dependent factor by using the values of the independent factors. Thus, it is pertinent to distinguish the two to make the prediction and analysis easier. Notably, the dependent variable is assessed through a continuous measurement scale like from 0-100, while the independent variable is evaluated on a categorical basis or using a continuous measurement. The sign of a given data set explains the relationship that exists between the given data. For instance, a negative correlation coefficient shows that the variables are inversely proportional. In effect, when one variable decreases, the other increases. On the other hand, a positive correlation coefficient shows that the data set is directly proportional. Thus, when one variable increases, the value of the other value also increases.